2012
Jeff Dean and Andrew Ng conduct an experiment using a massive neural network with 10 million unlabeled images sourced from YouTube videos.36 During the experiment, the network, without prior labeling, learns to recognize patterns in the data and "to our amusement," one neuron becomes particularly responsive to images of cats. This discovery is a demonstration of unsupervised learning—showing how deep neural networks can autonomously learn features from vast amounts of data.
Researchers from the University of Toronto, led by Geoffrey Hinton, design a convolutional neural network that achieves a breakthrough result in the ImageNet Large Scale Visual Recognition Challenge.37 Their CNN, known as AlexNet achieves a 16% error rate, a substantial improvement over the previous year's best result of 25%. This achievement marks a turning point for deep learning in computer vision, proving that CNNs can outperform traditional image classification methods when trained on large datasets.